Conduct experiments on supervised learning with Adaptive agent

Current idea in #40 is to work with online agents that learn while optimization is run, generally in on-policy RL-fashion.

Another possibility is to try Supervised Learning paradigm:
- Collect a dataset of histories *for certain class of tasks* (like for molecular findings)
- Train a supervised agent on it (algorithm can be anything, from Contextual MAB to linear regression to simple+fast neural SOTA-architecture)
- Validate GOLEM using learnt action probabilities, see if hypothesis holds: GOLEM should run faster & better with trained agent

Tasks can be (a) search of synthetic graphs of different kinds (b) molecular search